查询结果:   刘付斌,冯丽娜.基于EEMD与RBF神经网络的网络流量预测[J].计算机应用与软件,2014,31(6):72 - 74,83.
中文标题
基于EEMD与RBF神经网络的网络流量预测
发表栏目
应用技术与研究
摘要点击数
762
英文标题
NETWORK TRAFFIC PREDICTION BASED ON EEMD and RBF NEURAL NETWORKS
作 者
刘付斌 冯丽娜 Liu Fubin Feng Li’na
作者单位
安阳师范学院物理与电气工程学院 河南 安阳 455000     
英文单位
School of Physics and Electrical Engineering,Anyang Normal University Anyang 455000,Henan,China     
关键词
流量预测 整体平均经验模态分解 RBF神经网络
Keywords
Traffic forecast Ensemble empirical mode decomposition RBF neural network
基金项目
作者资料
刘付斌,讲师,主研领域:计算机网络。冯丽娜,讲师。 。
文章摘要
网络流量预测对于网络性能和服务质量的提高具有重要意义。提出一种基于整体平均经验模态分解EEMD(Ensemble Empirical Mode Decomposition)与径向基函数RBF(Radial Basis Function)神经网络的预测模型,利用EEMD将长相关流量转化为短相关流量并应用RBF神经网络模型对流量数据进行建模及预测,不仅降低了算法的复杂度,而且有利于网络流量的实时预测。仿真试验结果表明,相比于自回归分数综合滑动平均模型FARIMA(Fractional AutoRegressive Integrated Moving Average Mode)、RBF神经网络模型及EMD(Empirical Mode Decomposition)与自回归滑动平均模型ARMA(AutoRegressive Moving Average Model),该模型具有更高的预测精度和良好的自适应性。
Abstract
Network traffic prediction plays an important part in the improvement of network performance and service quality.The paper proposes a forecast model based on ensemble empirical mode decomposition (EEMD) and RBF neural network.The method translates long-term dependence traffic to short-term dependence traffic by EEMD and applies RBF neural network model to modeling and forecasting the traffic data.In this way it not only reduces the complexity of the algorithm,but also does favor for the real-time forecasting of network traffic.Simulation result shows that compared with the fractional autoregressive integrated moving average model (FARIMA),the RBF neural network model and the EMD with autoregressive moving average model (ARMA),the proposed model forecasts more accurately and is more adaptive.
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